SWFPGrowth
- class PAMI.weightedFrequentNeighbourhoodPattern.basic.SWFPGrowth.SWFPGrowth(iFile: str | DataFrame, nFile: str | DataFrame, minWS: int | float | str, sep='\t')[source]
Bases:
_weightedFrequentSpatialPatterns- Description:
SWFPGrowth is an algorithm to mine the weighted spatial frequent patterns in spatiotemporal databases.
- Reference:
R. Uday Kiran, P. P. C. Reddy, K. Zettsu, M. Toyoda, M. Kitsuregawa and P. Krishna Reddy, “Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases,” 2019 International Conference on Data Mining Workshops (ICDMW), 2019, pp. 987-996, doi: 10.1109/ICDMW.2019.00143.
- Parameters:
iFile – str : Name of the Input file to mine complete set of weighted Frequent Neighbourhood Patterns.
oFile – str : Name of the output file to store complete set of weighted Frequent Neighbourhood Patterns.
minSup – int or str or float: minimum support thresholds were tuned to find the appropriate ranges in the limited memory
sep – str : This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
maxper – floot : where maxPer represents the maximum periodicity threshold value specified by the user.
- Attributes:
- iFilefile
Input file name or path of the input file
- minWS: float or int or str
The user can specify minWS either in count or proportion of database size. If the program detects the data type of minWS is integer, then it treats minWS is expressed in count. Otherwise, it will be treated as float. Example: minWS=10 will be treated as integer, while minWS=10.0 will be treated as float
- minWeight: float or int or str
The user can specify minWeight either in count or proportion of database size. If the program detects the data type of minWeight is integer, then it treats minWeight is expressed in count. Otherwise, it will be treated as float. Example: minWeight=10 will be treated as integer, while minWeight=10.0 will be treated as float
- sepstr
This variable is used to distinguish items from one another in a transaction. The default separator is tab space or . However, the users can override their default separator.
- oFilefile
Name of the output file or the path of the output file
- startTime:float
To record the start time of the mining process
- endTime:float
To record the completion time of the mining process
- memoryUSSfloat
To store the total amount of USS memory consumed by the program
- memoryRSSfloat
To store the total amount of RSS memory consumed by the program
- Databaselist
To store the transactions of a database in list
- mapSupportDictionary
To maintain the information of item and their frequency
- lnoint
it represents the total no of transactions
- treeclass
it represents the Tree class
- finalPatternsdict
it represents to store the patterns
:Methods :
- mine()
Mining process will start from here
- getPatterns()
Complete set of patterns will be retrieved with this function
- save(oFile)
Complete set of frequent patterns will be loaded in to an output file
- getPatternsAsDataFrame()
Complete set of frequent patterns will be loaded in to a dataframe
- getMemoryUSS()
Total amount of USS memory consumed by the mining process will be retrieved from this function
- getMemoryRSS()
Total amount of RSS memory consumed by the mining process will be retrieved from this function
- getRuntime()
Total amount of runtime taken by the mining process will be retrieved from this function
- creatingItemSets()
Scans the dataset or dataframes and stores in list format
- frequentOneItem()
Extracts the one-frequent patterns from transactions
Methods to execute code on terminal
Format: (.venv) $ python3 SWFPGrowth.py <inputFile> <weightFile> <outputFile> <minSup> <minWeight> Example usage : (.venv) $ python3 SWFPGrowth.py sampleDB.txt weightFile.txt patterns.txt 10 2 .. note:: minSup will be considered in support count or frequency
Importing this algorithm into a python program
from PAMI.weightFrequentNeighbourhoodPattern.basic import SWFPGrowth as alg obj = alg.SWFPGrowth(iFile, wFile, nFile, minSup, minWeight, seperator) obj.startMine() frequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(frequentPatterns)) obj.save(oFile) Df = obj.getPatternsAsDataFrame() memUSS = obj.getmemoryUSS() print("Total Memory in USS:", memUSS) memRSS = obj.getMemoryRSS() print("Total Memory in RSS", memRSS) run = obj.getRuntime() print("Total ExecutionTime in seconds:", run)
Credits:
The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.
- getMemoryRSS() float[source]
Total amount of RSS memory consumed by the mining process will be retrieved from this function
- Returns:
returning RSS memory consumed by the mining process
- Return type:
float
- getMemoryUSS() float[source]
Total amount of USS memory consumed by the mining process will be retrieved from this function
- Returns:
returning USS memory consumed by the mining process
- Return type:
float
- getPatterns() Dict[str, float][source]
Function to send the set of frequent patterns after completion of the mining process
- Returns:
returning frequent patterns
- Return type:
dict
- getPatternsAsDataFrame() DataFrame[source]
Storing final frequent patterns in a dataframe
- Returns:
returning frequent patterns in a dataframe
- Return type:
pd.DataFrame
- getRuntime() float[source]
Calculating the total amount of runtime taken by the mining process
- Returns:
returning total amount of runtime taken by the mining process
- Return type:
float